ML BASED STRESS DETECTION VIA HEART RATE VARIABILITY
Alt Text: ML-Based Stress Detection via Heart Rate Variability
Title: ML-Based Stress Detection via Heart Rate Variability
Caption: Using machine learning to analyze heart rate variability for stress detection.
Description: This research focuses on developing a machine learning model to detect stress levels accurately based on heart rate variability (HRV) indicators, highlighting the relationship between HRV and relaxation or stress.
Keywords: Machine Learning, Stress Detection, Heart Rate Variability, HRV Analysis, Predictive Health Monitoring
International Journal of Engineering and Techniques – Volume 10 Issue 3, June 2024
Dr. R. Rajani1, CH. Vasavi2
1Professor, Department of Computer Science & Engineering, Geethanjali Institute of Science and Technology, Gangavaram, Andhra Pradesh, India.
2Assistant Professor, Department of Computer Science & Engineering, Geethanjali Institute of Science and Technology, Gangavaram, Andhra Pradesh, India.
Abstract
Stress is a natural response to pressure, but chronic stress can lead to mental health issues. Stress measurement involves physiological parameters like Heart Rate Variability (HRV), which is distinct from heart rate. HRV represents the variation in time intervals between successive heartbeats, decreasing during stress and increasing during relaxation. This study introduces a machine learning model capable of accurately detecting stress levels based on HRV indicators, offering potential improvements in predictive health monitoring.
Keywords
Machine Learning, Stress Detection, Heart Rate Variability, HRV Analysis, Predictive Health Monitoring
How to Cite
Rajani, R., Vasavi, C.H., “ML-Based Stress Detection via Heart Rate Variability,” International Journal of Engineering and Techniques, Volume 10, Issue 3, June 2024. ISSN 2395-1303
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